点过程模型中向量参数极大似然估计的渐近性质

点过程是一个应用广泛的统计模型，在医学，社会学，经济学，电子与通信科学以及软件与硬件可靠性等许多科学领域都能找到应用点过程的例子，在这些实际应用中，一般是根据问题的实际背景假定模型具有一定的参数形式，然后根据观测数据给出未知参数的极大似然估计值以推断事物发展的客观规律，我们知道，一种估计量是否收敛以及收敛速度的快慢，是决定这种估计量好坏的最为重要的标准，本文对于一般的点过程模型中向量参数极大似然估计（MLE）首先给出了一个保证其强相合的较为广泛的充分性条件，然后在进一步的条件下得到了重对数型的收敛速度。     

潜周期模型中频率变点的估计

本文用逐段计算周期图的办法研究了带有频率交点的潜周期模型估计问题，给出了交点数目、位置和潜频率的强相合估计．数值模拟表明本文方法对变点个数和潜频率估计很好，但是要准确估计交点位置需要较大样本量，估计对于噪声水平较高情况仍然有效。     

一类基于秩次的稳健线性回归估计与诊断方法

线性回归模型的误差项不服从正态分布或存在多个离群点时,可以将残差秩次的某些函数作为权重引入估计模型来减少离群点的不良影响。本文从参数估计、稳健性质、回归诊断等方面对基于残差秩次的一类稳健回归方法进行介绍.通过模拟研究和实例分析表明,R和GR估计是一种估计效率较高的稳健回归方法,其中GR估计可同时避免X与Y空间离群点,而高失效点HBR估计可通过控制某个参数在稳健性与估计效率之间进行折衷.     

拟蒙特卡罗方法用于估计多元回归函数

拟蒙特卡罗（QMC）方法被广泛用于解决数值分析和统计学中的各种问题，比如数值积分，最优化，试验设计，随机过程的模拟等．本文研究该方法在估计多元回归函数中的应用．证明了，在相当一般的条件下，均匀设计（或者，“代表点设计”）与回归函数傅里叶系数的QMC估计（对应地，使用拟随机重要性抽样的QMC估计）一起，构成一个回归函数的渐近最优投影估计．     

Logistic曲线拟合方法研究

Logistic模型具有广泛的实用性。本文推导了用三点法估计该模型中参数K值的公式 ,并提出了估计K值的新方法一四点法和拐点法。用 3种方法求出K值后 ,再用线性化回归获得另外两个参数a、r,应用实例研究表明 :3种方法都可得到较高拟合精度 ,其中以四点法最优。而且 ,以这些方法得到的参数估计值作为初始值进行非线性回归 ,易获得 3个参数的最优估计。     

方向数据回归函数核估计平均偏差的指数界

本文讨论方向数据回归函数核估计的平均偏差，在关于应变量、核函数及窗宽的温和假定下得到了 这类估计的平均偏差的指数界．     

基于最小截平方和估计的监测数据分析方法

水工程安全监测数据中不可避免地存在离群点,而应用最为广泛的最小二乘法(least square,LS)不具备剔除离群点的能力,反而更易吸收离群点,使回归曲线严重偏离实际。针对LS在此方面的缺陷,本文在最小化残差平方和理论的基础上,提出采用最小截平方和估计(least trimmed squares,LTS)方法来构建水工程安全监控模型。根据实际工程的监测资料并对监测资料分析处理,剔除离群点得到最优数据群。通过求解最优数据群的回归系数,进而得到最接近实际数据的拟合曲线。相比于LS估计,LTS估计所得结果更具有合理性、稳健性,且能够显著提高数据的预测精度。因此,LTS估计在水工程安全监测等数据分析中具有良好的应用前景。     

一类Kaplan-Meier估计泛函的一些大样本性质

设F,G分别表示某寿命随机变量与删失随机变量的分布函数,在不假定F、G连续的情况下该文使用点过程鞅方法证明了Kaplan－Meier估计的一类泛函的渐近正态性,并建立了一个均方误差不等式和一个概率不等式．     

非线性回归模型M估计的迭代公式及其收敛性

本文研究了非线性回归模型M估计的Gauss-Newton迭代公式及其改进形式的收敛性问题。把Jeunrich和Gallant等人关于最小二乘估计的结果推广到M估计的情形。本文的证明显示,这些结果还可以推广到更广泛的模型和更一般的估计。本文的实例说明,改进的Gauss-Newton迭代法对于求解非线性回归的M估计是比较有效的,M估计对于消除异常点的影响育显著的作用。     

基于稳健回归的经济增长数据可靠性评估

为了顺应改革开放以来经济规模和结构的不断调整,我国的统计体系发生了较大的变化。部分经济指标在不同层面的汇总结果存在差异,导致一些学者和组织对我国公布的经济增长数据质量提出质疑。因此,对我国经济增长数据的可靠性进行检测,成为学界持续关注的热点话题。在过去的研究中,很多学者使用了传统的回归方法,但这些方法容易受到异常值的影响,造成结果的可靠性较低。本文提出一种基于MRCD估计和MM估计的稳健回归方法,使用2019年中国大陆31个省级行政区域的GDP增长率和14个经济增长相关指标的增长率数据对中国的经济数据质量进行了评估。研究结果表明,该模型不仅提高了对异常值的识别能力,还降低了异常值对回归估计值的影响,因而同时提高了结果的可靠性和实际应用能力。实证结果表明,我国的经济增长数据是有质量保证的。     

Nonlinear least-squares estimation

The paper uses empirical process techniques to study the asymptotics of the least-squares estimator (LSE) for the fitting of a nonlinear regression function. By combining and extending ideas of Wu and Van de Geer, it establishes new consistency and central limit theorems that hold under only second moment assumptions on the errors. An application to a delicate example of Wu's illustrates the use of the new theorems, leading to a normal approximation to the LSE with unusual logarithmic rescalings.     

最小二乘估计关于误差分布的稳健性

对于设计矩阵$X$是列降秩的线性统计模型, 本文讨论了最小二乘估计关于误差分布的稳健性, 给出了误差分布的最大类, 使得误差项的分布在此范围内变动时, 最小二乘估计在均方误差矩阵准则下是最优估计.     

PARAMETER ESTIMATION FOR A CLASS OF STOCHASTIC DIFFERENTIAL EQUATIONS DRIVEN BY SMALL STABLE NOISES FROM DISCRETE OBSERVATIONS

We study the least squares estimation of drift parameters for a class of stochastic differential equations driven by small α-stable noises, observed at n regularly spaced time points ti=i/n, i=1,···,n on [0, 1]. Under some regularity conditions, we obtain the consistency and the rate of convergence of the least squares estimator (LSE) when a small dispersion parameter ε→0 and n→∞ simultaneously. The asymptotic distribution of the LSE in our setting is shown to be stable, which is completely different from the classical cases where asymptotic distributions are normal.     

回归系数的广义岭型主相关估计及其优良性

In this paper, we propose a new biased estimator of the regression parameters, the generalized ridge and principal correlation estimator. We present its some properties and prove that it is superior to LSE （least squares estimator）, principal correlation estimator, ridge and principal correlation estimator under MSE （mean squares error） and PMC （Pitman closeness） criterion, respectively.     

Accuracy and Stability of the Null Space Method for Solving the Equality Constrained Least Squares Problem

The null space method is a standard method for solving the linear least squares problem subject to equality constraints (the LSE problem). We show that three variants of the method, including one used in LAPACK that is based on the generalized QR factorization, are numerically stable. We derive two perturbation bounds for the LSE problem: one of standard form that is not attainable, and a bound that yields the condition number of the LSE problem to within a small constant factor. By combining the backward error analysis and perturbation bounds we derive an approximate forward error bound suitable for practical computation. Numerical experiments are given to illustrate the sharpness of this bound.     

Time series regression models with locally stationary disturbance

Time series linear regression models with stationary residuals are a well studied topic, and have been widely applied in a number of fields. However, the stationarity assumption on the residuals seems to be restrictive. The analysis of relatively long stretches of time series data that may contain changes in the spectrum is of interest in many areas. Locally stationary processes have time-varying spectral densities, the structure of which smoothly changes in time. Therefore, we extend the model to the case of locally stationary residuals. The best linear unbiased estimator (BLUE) of vector of regression coefficients involves the residual covariance matrix which is usually unknown. Hence, we often use the least squares estimator (LSE), which is always feasible, but in general is not efficient. We evaluate the asymptotic covariance matrices of the BLUE and the LSE. We also study the efficiency of the LSE relative to the BLUE. Numerical examples illustrate the situation under locally stationary disturbances.     

On Nikaido-Isoda type theorems for discounted stochastic games

We present a class of countable state space stochastic games with discontinuous payoff functions satisfying some assumptions similar to the ones of Nikaido and Isoda for one-stage games. We prove that these games possess stationary equilibria. We show that after adding some concavity assumptions these equilibria are nonrandomized. Further, we present an example of input (or production) dynamic game satisfying the assumptions of our model. We give a closed-form solution for this game.     

几种最小二乘估计（LSE）效率的偏序

研究了几种最小二乘估计（ＬＳＥ）效率的偏序，证明了均方误差比（ＭＳＥＲ）效率在几种效率度量中是最优的。     

倒向随机微分方程Lp解的性质

2003年Briand et al等在很一般的假设下建立了倒向随机微分方程（BSDEs）L^p解的存在唯一性定理.本文在此基础上得到了这种假设下一维BSDEs的L^p解的几个连续性质.     

Zur Kondition des linearen Ausgleichsproblems mit linearen Gleichungen als Nebenbedingungen

Summary F.L. Bauer has treated in several papers [1, 3, 4] the condition related to the solution of linear equations and to the algebraic eigenvalue problem. We study the condition for the linear least squares problem with linear equality constraints (problem LSE). A perturbation theory of problem LSE is presented and three condition numbers are defined. Problem LSE includes the linear least squares problem (problem LS). There are examples with identical solution of problem LSE and of problem LS. Sometimes the condition of problem LSE is better and sometimes the condition of problem LS is better. Several numerical tests illustrate the theory.

Herrn Prof. Dr. Dr. F.L. Bauer zum 60. Geburtstag gewidmet